Immune Infiltration Landscape Research Articles

A novel pyroptosis-related gene signature exhibits distinct immune cells infiltration landscape in Wilms’ tumor

BackgroundWilms’ tumor (WT) is the most common renal tumor in childhood. Pyroptosis, a type of inflammation-characterized and immune-related programmed cell death, has been extensively studied in multiple tumors. In the current study, we aim to construct a pyroptosis-related gene signature for predicting the prognosis of Wilms’ tumor.MethodsWe acquired RNA-seq data from TARGET kidney tumor projects for constructing a gene signature, and snRNA-seq data from GEO database for validating signature-constructing genes. Pyroptosis-related genes (PRGs) were collected from three online databases. We constructed the gene signature by Lasso Cox regression and then established a nomogram. Underlying mechanisms by which gene signature is related to overall survival states of patients were explored by immune cell infiltration analysis, differential expression analysis, and functional enrichment analysis.ResultsA pyroptosis-related gene signature was constructed with 14 PRGs, which has a moderate to high predicting capacity with 1-, 3-, and 5-year area under the curve (AUC) values of 0.78, 0.80, and 0.83, respectively. A prognosis-predicting nomogram was established by gender, stage, and risk score. Tumor-infiltrating immune cells were quantified by seven algorithms, and the expression of CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages are positively or negatively correlated with risk score. Two single nuclear RNA-seq samples of different histology were harnessed for validation. The distribution of signature genes was identified in various cell types.ConclusionsWe have established a pyroptosis-related 14-gene signature in WT. Moreover, the inherent roles of immune cells (CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages), functions of differentially expressed genes (tissue/organ development and intercellular communication), and status of signaling pathways (proteoglycans in cancer, signaling pathways regulating pluripotent of stem cells, and Wnt signaling pathway) have been elucidated, which might be employed as therapeutic targets in the future.

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.

Exploring Pyroptosis-related Signature Genes and Potential Drugs in Ulcerative Colitis by Transcriptome Data and Animal ExperimentalValidation.

Ulcerative colitis (UC) is an idiopathic, relapsing inflammatory disorder of the colonic mucosa. Pyroptosis contributes significantly to UC. However, the molecular mechanisms of UC remain unexplained. Herein, using transcriptome data and animal experimentalvalidation, we sought to explore pyroptosis-related molecular mechanisms, signature genes, and potential drugs in UC.Gene profiles (GSE48959, GSE59071, GSE53306, and GSE94648) were selected from the Gene Expression Omnibus (GEO) database, which contained samples derived from patients with active and inactive UC, as well as health controls. Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on microarrays to unravel the association between UC and pyroptosis. Then, differential expressed genes (DEGs) and pyroptosis-related DEGs were obtained by differential expression analyses and the public database. Subsequently, pyroptosis-related DEGs and their association with the immune infiltration landscape were analyzed using the CIBERSORT method. Besides, potential signature genes were selected by machine learning (ML) algorithms, and then validated by testing datasets which included samples of colonic mucosal tissue and peripheral blood. More importantly, the potential drug was screened based on this. And these signature genes and the drug effect were finally observed in the animal experiment.GSEA and KEGG enrichment analyses on key module genes derived from WGCNA revealed a close association between UC and pyroptosis. Then, a total of 20 pyroptosis-related DEGs of UC and 27 pyroptosis-related DEGs of active UC were screened. Next, 6 candidate genes (ZBP1, AIM2, IL1β, CASP1, TLR4, CASP11) in UC and 2 candidate genes (TLR4, CASP11) in active UC were respectively identified using the binary logistic regression(BLR), least absolute shrinkage and selection operator (LASSO), random forest (RF) analysis and artificial neural network (ANN), and these genes also showed high diagnostic specificity for UC in testing sets. Specially, TLR4 was elevated in UC and further elevated in active UC. The results of the drug screen revealed that six compounds (quercetin, cyclosporine, resveratrol, cisplatin, paclitaxel, rosiglitazone) could target TLR4, among which the effect of quercetin on intestinal pathology, pyroptosis and the expression of TLR4 in UC and active UC was further determined by the murine model.These findings demonstrated that pyroptosis may promote UC, and especially contributes to the activation of UC. Pyroptosis-related DEGs offer new ideas for the diagnosis of UC. Besides, quercetin was verified as an effective treatment for pyroptosis and intestinal inflammation. This study might enhance our comprehension on the pathogenic mechanism and diagnosis of UC and offer a treatment option for UC.

Diagnostic and predictive significance of the ferroptosis-related gene TXNIP in lung adenocarcinoma stem cells based on multi-omics

BackgroundLung cancer stands as the foremost cause of cancer-related fatalities globally. The presence of cancer stem cells (CSCs) poses a challenge, rendering current targeted tumor therapies ineffective. This study endeavors to investigate a novel therapeutic approach focusing on ferroptosis and delves into the expression of ferroptosis-related genes within lung CSCs. MethodsWe systematically examined RNA-seq datasets derived from lung tumor cells (LTCs) and lung cancer stem cells (LSCs), as previously investigated in our research. Our focus was on analyzing differentially expressed genes (DEGs) related to ferroptosis. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), we conducted functional analysis of these ferroptosis-related DEGs. Additionally, we employed protein‒protein interaction networks to identify hub genes. LC‒MS/MS analysis of LTCs and LSCs was conducted to pinpoint the crucial ferroptosis-related gene–thioredoxin-interacting protein (TXNIP).Further, we delved into the immune cell infiltration landscape of LTCs and LSCs, examining the correlation between TXNIP and lung adenocarcinoma (LUAD) using data from The Cancer Genome Atlas (TCGA) database. To complement these findings, we measured the expression levels of TXNIP, glutathione peroxidase 4(GPX4), nuclear receptor coactivator 4 (NCOA4) in LUAD tissues through immunohistochemistry (IHC) staining. ResultsA total of 651 DEGs were identified, with 17 of them being ferroptosis-related DEGs. These seventeen genes were categorized into four groups: driver genes, suppressor genes, unclassified genes, and inducer genes. Enrichment analysis revealed significant associations with oxidative stress, cell differentiation, tissue development, and cell death processes. The RNA-seq analysis demonstrated consistent gene expression patterns with protein expression, as evidenced by mass spectrometry analysis. Among the identified genes, SFN and TXNIP were singled out as hub genes, with TXNIP showing particularly noteworthy expression. The expression of the ferroptosis-related gene TXNIP exhibited correlations with the presence of an immunosuppressive microenvironment, TNM stages, and the degree of histological differentiation.Also, the ferroptosis-markers GPX4 and NCOA4 displayed correlations with LUAD. This comprehensive analysis underscores the significance of TXNIP in the context of ferroptosis-related processes and their potential implications in cancer development and progression. ConclusionThe investigation conducted in this study systematically delved into the role of the ferroptosis-related gene TXNIP in Lung CSCs. The identification of TXNIP as a potentially valuable biomarker in this context could have significant implications for refining prognostic assessments and optimizing therapeutic strategies for advanced lung cancer.

Transcriptomic analysis reveals molecular characterization and immune landscape of PANoptosis-related genes in atherosclerosis.

Atherosclerosis is a chronic inflammatory disease characterized by abnormal lipid deposition in the arteries. Programmed cell death is involved in the inflammatory response of atherosclerosis, but PANoptosis, as a new form of programmed cell death, is still unclear in atherosclerosis. This study explored the key PANoptosis-related genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis. We evaluated differentially expressed genes (DEGs) and immune infiltration landscape in atherosclerosis using microarray datasets and bioinformatics analysis. By intersecting PANoptosis-related genes from the GeneCards database with DEGs, we obtained a set of PANoptosis-related genes in atherosclerosis (PANoDEGs). Functional enrichment analysis of PANoDEGs was performed and protein-protein interaction (PPI) network of PANoDEGs was established. The machine learning algorithms were used to identify the key PANoDEGs closely linked to atherosclerosis. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of key PANoDEGs. CIBERSORT was used to analyze the immune infiltration patterns in atherosclerosis, and the Spearman method was used to study the relationship between key PANoDEGs and immune infiltration abundance. The single gene enrichment analysis of key PANoDEGs was investigated by GSEA. The transcription factors and target miRNAs of key PANoDEGs were predicted by Cytoscape and online database, respectively. The expression of key PANoDEGs was validated through animal and cell experiments. PANoDEGs in atherosclerosis were significantly enriched in apoptotic process, pyroptosis, necroptosis, cytosolic DNA-sensing pathway, NOD-like receptor signaling pathway, lipid and atherosclerosis. Four key PANoDEGs (ZBP1, SNHG6, DNM1L, and AIM2) were found to be closely related to atherosclerosis. The ROC curve analysis demonstrated that the key PANoDEGs had a strong diagnostic potential in distinguishing atherosclerotic samples from control samples. Immune cell infiltration analysis revealed that the proportion of initial B cells, plasma cells, CD4 memory resting T cells, and M1 macrophages was significantly higher in atherosclerotic tissues compared to normal tissues. Spearman analysis showed that key PANoDEGs showed strong correlations with immune cells such as T cells, macrophages, plasma cells, and mast cells. The regulatory networks of the four key PANoDEGs were established. The expression of key PANoDEGs was verified in further cell and animal experiments. This study evaluated the expression changes of PANoptosis-related genes in atherosclerosis, providing a reference direction for the study of PANoptosis in atherosclerosis and offering potential new avenues for further understanding the pathogenesis and treatment strategies of atherosclerosis.

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study.

Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

Abstract 3928: Genomic landscape and estimation of immune infiltration of soft tissue sarcoma histology subtypes from the ORIEN network

Abstract Sarcomas encompass a group of malignant diseases arising from mesenchymal origins. Given their rarity and diversity, a fundamental understanding of the genomic underpinnings for many sarcoma subtypes is still lacking. Studies are often limited to one or several of the more common subtypes or a narrow evaluation of a broader sampling. We therefore report on one of the largest comprehensive omics evaluation in sarcomas to date, including whole exome sequencing (WES; n = 1170) and RNA-sequencing (n = 983) of tissues from 29 different sarcoma histologic subtypes collected at 13 institutions in the US as part of the Oncology Research Information Exchange Network (ORIEN). We identified recurrent somatic mutations previously identified in sarcomas (e.g. TP53, KIT) as well as other cancer types (e.g. BRCA1). The burden of putatively pathogenic driver point mutations was higher in metastatic samples (median = 3) as compared to primary tumor samples (median = 2; p &amp;lt; 0.001). We observed frequent copy number alterations including whole genome doubling more commonly in metastatic compared to primary tumors (23.4% vs. 16.9%; p = 0.0.25). Inspection of gene expression dimensionality reduction (UMAP) showed separation of gastrointestinal stromal tumors (GISTs), leiomyosarcomas, myxoid liposarcomas, and well/de-differentiated liposarcomas from the other histologies. Differential expression analysis for these four histologies with gene set enrichment analysis highlights the diversity of disease-specific pathways and need for sarcoma subtype-specific translational focus. Estimation of immune cell abundances based on RNA-seq followed by hierarchical clustering identified five immune subtypes. The subtypes ranged from low (clusters A, B) to high (clusters D, E) immune infiltration with higher abundance of T, B, Natural Killer (NK), and myeloid cells (FDR &amp;lt; 0.01). Intermediate immune group C was predominantly composed by GISTs and marked by an enrichment for NK cells (FDR &amp;lt; 0.01) compared to all groups except the immune “hot” group E; however, this immune group exhibited modest infiltration by other immune cell types. Notably, we observed significant differences in the overall survival of patients with sarcomas in immune enriched (C, D, E) compared to immune depleted clusters (A, B; p = 0.002). In summary, we report the genomic and expressional landscape of over 1000 sarcomas, representing one of the largest comprehensive profiling efforts in this disease. We identify the mutational and copy number variation landscape and observe differences between primary and metastatic samples. We highlight expression pathways that are enriched in histologic subtypes that cluster most distinctly from others, providing a subtype-specific roadmap for further translational efforts. Finally, we define immune enriched or depleted sarcoma subgroupings that carry a prognostic impact. Citation Format: Alex C. Soupir, Oscar E. Ospina, Dale Hedges, Jamie K. Teer, Michael D. Radmacher, David M. McKean, Nathan Seligson, Martin McCarter, Breelyn Wilkey, Greg Riedlinger, John Groundland, Benjamin J. Miller, Bryan Schneider, Reema Patel, Abdul Rafeh-Naqash, Stephen Edge, Bodour Salhia, Chris Moskaluk, Maggy Johns, Michelle L. Churchman, Oliver Hampton, David Liebner, Brooke L. Fridley, Andrew S. Brohl. Genomic landscape and estimation of immune infiltration of soft tissue sarcoma histology subtypes from the ORIEN network [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3928.

Abstract 7046: Proteogenomic and immunologic profiles of head and neck cancer patients of African ancestry

Abstract Blacks/African Americans (BAAs) with head and neck squamous cell carcinoma (HNSCC) have worse survival than Whites. However, the complex molecular biomarkers and signatures of HNSCC among BAAs and molecular distinction associated with HNSCC disparities are largely unknown. Here we show the results of a multi-omics analysis of 500 patients (48 BAAs and 452 Whites). Comparative analyses between the racial groups were performed at the level of genomics, pathways, molecular subtype, tumor immune infiltration, protein alterations and prognosis. We found that overall survival in BAAs with HPV-unrelated HNSCC is significantly shorter than in White patients. Ancestry-related differential mutation spectrum, DNA methylation, gene and protein expression pattern and immune infiltration landscape were identified. Particularly, FAT1 mutation is significantly associated with its gene expression in BAA HNSCC and poor overall survival in this group. In BAA HNSCC, SALL3 gene expression is positively associated with its gene CNVs, while RTP4 gene expression is inversely correlated with its methylation. High EGFR_pY1068 and RAD50 levels correlate with poor prognosis in BAAs with HNSCC. The immune infiltration enrichment scores of Th17 cells, activated dendritic cells, monocytes and neutrophils in BAA HNSCC were significantly lower than those in White HNSCC. By combining multi-omics analysis and data validation using in-house-made tumor tissue arrays, this study comprehensively elucidates the genetic landscape, immune response, and tumor biology in BAA and White HNSCC patients, providing a molecular basis for future research in identifying appropriate screening modalities and therapeutic interventions leading to improved outcomes in BAAs with HNSCC. Citation Format: Fan Yang, Fanghui Chen, Chloe Shay, Nabil F. Saba, Yong Teng. Proteogenomic and immunologic profiles of head and neck cancer patients of African ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7046.

Profiling of immune infiltration landscape of ruptured intracranial aneurysm.

Previous research has indicated that the rupture of intracranial aneurysm (IA) is a significant contributor to mortality from stroke. The objective of this present study was to examine the infiltration patterns in ruptured intracranial aneurysm (RIA), with the aim of generating insights that could inform the development of effective immunotherapeutic approaches. To achieve this, we obtained Gene Expression Omnibus datasets pertaining to ruptured aneurysms, encompassing a total of 19 unruptured intracranial aneurysms (UIA) and 27 RIA. Subsequently, we conducted differential gene analysis and immune cell analysis specifically for the RIA. According to the conducted studies, the analysis has identified 10 hub genes within key modules. Through the utilization of Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology terms analyses, it has been established that genes exhibiting differential expression are associated with immune cell infiltration in the aneurysm wall. Furthermore, the implementation of the CIBERSORT algorithm has revealed that there are 22 distinct immune cells between RIA and tissues of UIA. IA samples contained a higher proportion of macrophages M1, mast cells resting, and CD4 naive T cells, while macrophages M0 and neutrophils were relatively lower in RIA compared with those in UIA. The current study initially identified highly conservative hub genes and immune cell infiltration patterns in IA. Data presented in the current study improved understanding of immune genes that drive IA which can be exploited in development of effective immunotherapies.

Identification of PDGFA as a Neutrophil-related Biomarker Linked to the Advancement of Diabetic Retinopathy through Integrated Bioinformatics Analysis.

The dysregulation of the innate immune system plays a crucial role in the development of Diabetic Retinopathy (DR). To gain an insight into the underlying mechanism of DR, it is essential to identify specific biomarkers associated with immune cell infiltration. In this study, we retrieved the GSE94019 and GSE60436 datasets from the Gene Expression Omnibus (GEO) database. By utilizing CIBERSORT, MCPcounter, and xCell algorithms, we conducted a comprehensive analysis of the immune cell infiltration landscape in DR. The limma package was employed to identify Differentially Expressed Necroptosis-related Genes (DENRGs). Subsequently, enrichment analysis was performed to investigate the potential functions of the DENRGs. To identify the core DENRGs, the CytoHubba plug-in in Cytoscape software was utilized. The expression levels of these core DENRGs were verified in an independent dataset. Our analysis identified 213 DENRGs, and among them, Platelet-derived Growth Factor subunit A (PDGFA) was identified as a core DENRG. Notably, the expression of PDGFA was found to be upregulated in DR, and this finding was further validated in the GSE102485 dataset. Additionally, the results of GSVA and GSEA revealed that in the high PDGFA group, there was activation of pathways related to inflammation and the immune system. Moreover, analysis of immune infiltration demonstrated a significant association between PDGFA gene expression and the infiltration levels of specific immune cells, including basophils, macrophages M1, macrophages, neutrophils, monocytes, NK cells, and B cells. The involvement of neutrophils in the development and progression of DR is suggested. PDGFA has emerged as a potential marker and is linked to the infiltration of immune cells in DR. These findings shed new light on the underlying mechanisms of DR.

Bioinformatics analysis characterizes immune infiltration landscape and identifies potential blood biomarkers for heart transplantation

Background: Cardiac allograft rejection (AR) remains a significant complication following heart transplantation. The primary objective of our study is to gain a comprehensive understanding of the fundamental mechanisms involved in AR and identify possible therapeutic targets.Methods: We acquired the GSE87301 dataset from the Gene Expression Omnibus database. In GSE87301, a comparison was conducted on blood samples from patients with and without cardiac allograft rejection (AR and NAR) to detect differentially expressed genes (DEGs). Enrichment analysis was conducted to identify the pathways that show significant enrichment during AR. Machine learning techniques, including the least absolute shrinkage and selection operator regression (LASSO) and random forest (RF) algorithms, were employed to identify potential genes for the diagnosis of AR. The diagnostic value was evaluated using a nomogram and receiver operating characteristic (ROC) curve. Additionally, immune cell infiltration was analyzed to explore any dysregulation of immune cells in AR.Results: A total of 114 DEGs were identified from the GSE87301 dataset. These DEGs were mainly found to be enriched in pathways related to the immune system. To identify the signature genes, the LASSO and RF algorithms were used, and four genes, namely ALAS2, HBD, EPB42, and FECH, were identified. The performance of these signature genes was evaluated using the receiver operating characteristic curve (ROC) analysis, which showed that the area under the curve (AUC) values for ALAS2, HBD, EPB42, and FECH were 0.906, 0.881, 0.900, and 0.856, respectively. These findings were further confirmed in the independent datasets and clinical samples. The selection of these specific genes was made to construct a nomogram, which demonstrated excellent diagnostic ability. Additionally, the results of the single-sample gene set enrichment analysis (ssGSEA) revealed that these genes may be involved in immune cell infiltration.Conclusion: We identified four signature genes (ALAS2, HBD, EPB42, and FECH) as potential peripheral blood diagnostic candidates for AR diagnosis. Additionally, a nomogram was constructed to aid in the diagnosis of heart transplantation. This study offers valuable insights into the identification of candidate genes for heart transplantation using peripheral blood samples.

Integrated bioinformatics analysis identifies autophagy-associated genes as candidate biomarkers and reveals the immune infiltration landscape in psoriasis

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the “limma” package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. CytoHubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. Subsequently, a series of bioinformatics analyses were employed, including protein–protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. Immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease.

Development and verification of a novel immunogenic cell death-related signature for predicting the prognosis and immune infiltration in triple-negative breast cancer.

Insufficient understanding of the pathogenesis and tumor immunology of triple-negative breast cancer (TNBC) has limited the development of immunotherapy. The importance of tumor microenvironment (TME) in immunotyping, prognostic assessment and immunotherapy efficacy of cancer has been emphasized, however, potential immunogenic cell death (ICD) related genes function in TME of TNBC has been rarely investigated. To initially explore the role and related mechanisms of ICD in TNBC, especially the role played in the TME of TNBC, and to identify different relevant subtypes based on ICD, and then develop an ICD-related risk score to predict each TNBC patient TME status, prognosis and immunotherapy response. In this study, we identified distinct ICD-related modification patterns based on 158 TNBC cases in the TCGA-TNBC cohort. We then investigated the possible correlation between ICD-related modification patterns and TME cell infiltration characteristics in TNBC. By using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis, we created a risk scoring system (ICD score) to quantifiably evaluate the impact of ICD-related modification patterns in individual TNBC patient. Two different ICD-related modification patterns were found with significant differences in immune infiltration. Lower ICD score was correlated with higher immune infiltration, tumor mutational burden and significantly enriched in immune-related pathways, indicating a strong ability to activate immune response, which might account for relatively favorable prognosis of TNBC patients and could serve as a predictor to select suitable candidates for immunotherapy. We used two independent cohorts, GSE58812 cohort and Metabric cohort to validate prognosis and immunohistochemistry for preliminary in vitro validation. This study evidenced that the ICD-related modification patterns might exert pivotal roles in the immune infiltration landscape of TNBC and ICD score might act as potential predictors of prognostic assessment and immunotherapy response. This research provides unique insights for individualize immune treatment strategies and promising immunotherapy candidates screening.

Novel diagnostic biomarkers related to necroptosis and immune infiltration landscape in acute myocardial infarction.

Acute myocardial infarction (AMI) can occur suddenly, which may induce deadly outcomes, and the population suffering from AMI presents a younger trend. Necroptosis, the new cell necrosis type, is associated with the pathogenic mechanisms of diverse cardiovascular diseases (CVDs). Its diagnostic value and molecular mechanisms in AMI are still unclear. Objective: This study focused on determining key necroptosis-related genes as well as immune infiltration in AMI. We first examined the GSE66360 dataset for identifying necroptosis-related differentially expressed genes (NRDEGs). Thereafter, GO and functional annotation were performed, then a PPI network was built. In addition, "CIBERSORT" in R was applied in comparing different immune infiltration degrees in AMI compared with control groups. The receiver operating characteristic (ROC) curve was plotted to evaluate whether hub NRDEGs could be used in AMI diagnosis. Associations of immune cells with candidate NRDEGs biomarkers were examined by Spearman analysis. Finally, hub NRDEGs were validated by cell qPCR assays and another two datasets. A total of 15 NRDEGs were identified and multiple enrichment terms associated with necroptosis were discovered through GO and KEGG analysis. Upon module analysis, 10 hub NRDEGs were filtered out, and the top six hub NRDEGs were identified after ROC analysis. These top six NRDEGs might have a certain effect on modulating immune infiltrating cells, especially for mast cells activated, NK cells activated and neutrophils. Finally, two AMI datasets and qPCR assay came to identical findings. Our results offer the reliable molecular biomarkers and new perspectives for necroptosis in AMI, which lay a certain foundation for developing novel anti-AMI therapeutic targets.

Validation and identification of anoikis-related lncRNA signatures for improving prognosis in clear cell renal cell carcinoma.

Clear cell carcinoma (ccRCC) usually has a high metastasis rate and high mortality rate. To enable precise risk stratification, there is a need for novel biomarkers. As one form of apoptosis, anoikis results from the disruption of cell-cell connection or cell-ECM attachment. However, the impact of anoikis-related lncRNAs on ccRCC has not yet received adequate attention. The study utilized univariate Cox regression analysis in order to identify the overall survival (OS) associated anoikis-related lncRNAs (ARLs), followed by the LASSO algorithm for selection. On this basis, a risk model was subsequently established using five anoikis-related lncRNAs. To dig the inner molecular mechanism, KEGG, GO, and GSVA analyses were conducted. Additionally, the immune infiltration landscape was estimated using the ESTIMATE, CIBERSORT, and ssGSEA algorithms. The study constructed a novel risk model based on five ARLs (AC092611.2, AC027601.2, AC103809.1, AL133215.2, and AL162586.1). Patients categorized as low-risk exhibited significantly better OS. Notably, the study observed marked different immune infiltration landscapes and drug sensitivity by risk stratification. Additionally, the study preliminarily explored potential signal pathways associated with risk stratification. The study exhibited the crucial role of ARLs in the carcinogenesis of ccRCC, potentially through differential immune infiltration. Furthermore, the established risk model could serve as a valuable stratification factor for predicting OS prognosis.

Prognostic significance and immune escape implication of tumor-infiltrating neutrophil plasticity in human head and neck squamous cell carcinoma.

Tumor-infiltrating neutrophils play a crucial role in the progression of head and neck squamous cell carcinoma (HNSCC). Here, we aimed to statistically quantify the plasticity of HNSCC-infiltrating N2/N1 neutrophils and examine its impacts on survival and immune infiltration landscape. A retrospective study of 80 patients who underwent curative surgical resection for HNSCC between 2014 and 2017 was conducted in this study. HNSCC-infiltrating neutrophil phenotypes were classified using immunofluorescence staining, and the N2/N1 neutrophil plasticity was evaluated via the ratio of N2/N1 neutrophils. We then assessed the correlations between N2/N1 neutrophil plasticity, clinicopathological characteristics, and immune infiltration landscape using rigorous statistical methods. Infiltration variations of N1 and N2 neutrophils were observed between the tumor nest (TN) and tumor stroma (TS), with TN exhibiting higher N2 neutrophil infiltration and lower N1 neutrophil infiltration. High ratios of N2/N1 neutrophils were correlated with advanced TNM stage, large tumor size and invasion of adjacent tissue. High infiltration of N2 neutrophils was associated with decreased overall and relapse-free survival, which were opposite for N1 neutrophils. The independent prognostic role of N2/N1 neutrophil plasticity, particularly within the TN region, was confirmed by multivariate analyses. Moreover, the ratio of N2/N1 neutrophils within the TN region showed correlations with high CD8+ T cells infiltration and low FOXP3+ Tregs infiltration. We identify HNSCC-infiltrating N2/N1 neutrophil plasticity as a crucial prognostic indictor which potentially reflects the tumor microenvironment (TME) and immune escape landscape within HNSCC tissues. Further investigations and validations may provide novel therapeutic strategies for personalized immunomodulation in HNSCC patients.

New insights into the role of mitochondrial metabolic dysregulation and immune infiltration in septic cardiomyopathy by integrated bioinformatics analysis and experimental validation

BackgroundSeptic cardiomyopathy (SCM), a common cardiovascular comorbidity of sepsis, has emerged among the leading causes of death in patients with sepsis. SCM’s pathogenesis is strongly affected by mitochondrial metabolic dysregulation and immune infiltration disorder. However, the specific mechanisms and their intricate interactions in SCM remain unclear. This study employed bioinformatics analysis and drug discovery approaches to identify the regulatory molecules, distinct functions, and underlying interactions of mitochondrial metabolism and immune microenvironment, along with potential interventional strategies in SCM.MethodsGSE79962, GSE171546, and GSE167363 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and module genes were identified using Limma and Weighted Correlation Network Analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms, including support vector machine–recursive feature elimination (SVM–RFE), least absolute shrinkage and selection operator (LASSO) regression, and random forest, were used to screen mitochondria-related hub genes for early diagnosis of SCM. Subsequently, a nomogram was developed based on six hub genes. The immunological landscape was evaluated by single-sample gene set enrichment analysis (ssGSEA). We also explored the expression pattern of hub genes and distribution of mitochondria/inflammation-related pathways in UMAP plots of single-cell dataset. Potential drugs were explored using the Drug Signatures Database (DSigDB). In vivo and in vitro experiments were performed to validate the pathogenetic mechanism of SCM and the therapeutic efficacy of candidate drugs.ResultsSix hub mitochondria-related DEGs [MitoDEGs; translocase of inner mitochondrial membrane domain-containing 1 (TIMMDC1), mitochondrial ribosomal protein S31 (MRPS31), F-box only protein 7 (FBXO7), phosphatidylglycerophosphate synthase 1 (PGS1), LYR motif containing 7 (LYRM7), and mitochondrial chaperone BCS1 (BCS1L)] were identified. The diagnostic nomogram model based on the six hub genes demonstrated high reliability and validity in both the training and validation sets. The immunological microenvironment differed between SCM and control groups. The Spearman correlation analysis revealed that hub MitoDEGs were significantly associated with the infiltration of immune cells. Upregulated hub genes showed remarkably high expression in the naive/memory B cell, CD14+ monocyte, and plasma cell subgroup, evidenced by the feature plot. The distribution of mitochondria/inflammation-related pathways varied across subgroups among control and SCM individuals. Metformin was predicted to be the most promising drug with the highest combined score. Its efficacy in restoring mitochondrial function and suppressing inflammatory responses has also been validated.ConclusionsThis study presents a comprehensive mitochondrial metabolism and immune infiltration landscape in SCM, providing a potential novel direction for the pathogenesis and medical intervention of SCM.

P699 Identification of A Novel Activated NK-Associated Gene Score Associated with Diagnosis and Biological Therapy Response in Ulcerative Colitis

Abstract Background Natural killer (NK) cells are associated with the pathogenesis of ulcerative colitis (UC), while their precise contributions remain unclear. The present study sought to investigate the diagnostic value of activated NK-associated gene score (ANAG score) in UC and evaluate its predictive value in biological therapy response. Methods Bulk RNA-seq datasets were obtained from the Gene Expression Omnibus (GEO). GSE11223, GSE87466, and GSE92415 were integrated for diagnosis value analysis. GSE206285 and GSE92415 were used to analyze the prediction of response to ustekinumab (UST) and golimumab (GLM), respectively. The immune infiltration landscape was estimated by single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Differentially expressed genes (DEGs) correlated with activated NK cells were identified as activated NK-associated genes (ANAGs). Key ANAGs were screened by protein-protein interaction (PPI) analysis and least absolute shrinkage and selection operator (LASSO) regression. Results The immune infiltration analysis revealed a higher abundance of activated NK cells in non-inflamed UC tissues. 54 DEGs correlated with activated NK cells were identified as ANAGs. PPI analysis and LASSO regression were utilized to screen out 4 key ANAGs (SELP, TIMP1, MMP7, and ABCG2). ANAG score was established with the following formula: ANAG score = 0.628 * SELP + 1.574 * TIMP1 + 0.740 * MMP7 - 0.361 * ABCG2 – 18.940. The ANAG scores were significantly higher in inflamed tissues, as well as in biological therapy non-responders (NR) tissues before treatment. The ANAG score demonstrated excellent diagnostic value (AUC = 0.979). Furthermore, according to quartile stratification, patients with a higher ANAG score before treatment were more likely to experience lack of response to ustekinumab and golimumab. Conclusion This study established a novel ANAG score with the ability to precisely diagnose UC and distinguish the efficacy of biological treatment.

Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model based on mitochondria-related genes(MRGs) for preeclampsia using machine learning and further investigate the association of the MRGs and immune infiltration landscape in preeclampsia. In this research, we analyzed GSE75010 database and screened 552 DE-MRGs between preeclampsia samples and normal samples. Enrichment assays indicated that 552 DE-MRGs were mainly related to energy metabolism pathway and several different diseases. Then, we performed LASSO and SVM-RFE and identified three critical diagnostic genes for preeclampsia, including CPOX, DEGS1 and SH3BP5. In addition, we developed a novel diagnostic model using the above three genes and its diagnostic value was confirmed in GSE44711, GSE75010 datasets and our cohorts. Importantly, the results of RT-PCR confirmed the expressions of CPOX, DEGS1 and SH3BP5 were distinctly increased in preeclampsia samples compared with normal samples. The results of the CIBERSORT algorithm revealed a striking dissimilarity between the immune cells found in preeclampsia samples and those found in normal samples. In addition, we found that the levels of SH3BP5 were closely associated with several immune cells, highlighting its potential involved in immune microenvironment of preeclampsia. Overall, this study has provided a novel diagnostic model and diagnostic genes for preeclampsia while also revealing the association between MRGs and immune infiltration. These findings offer valuable insights for further research and treatment of preeclampsia.

Identification and characterization of two immune-related subtypes in human chronic kidney disease

BackgroundImmune response plays a vital role in the initiation and development of chronic kidney disease (CKD). Detailed mechanisms and specific immune-related biomarkers of CKD need further clarification. We aimed to identify and characterize immune-related infiltrates that are implicated in the CKD development using a bioinformatics method. MethodsThe expression profiles of GSE66494 dataset were acquired from the Gene Expression Omnibus (GEO) database. Patients with CKD were divided into low- vs. high-immune subtypes based on their immune score. Based on such analysis, we identified differentially expressed genes (DEGs) of low- and high-immune subtypes. The weight gene co-expression network analysis (WGCNA) was used to identify immune-associated modules between two subtypes. The gene set enriched (GSEA) and variation (GSVA) analyses were correlated with their functional types using the molecular complex detection (MCODE) method. Finally, the immune infiltration landscape between subtypes was revealed using the xCell algorithm. ResultsThe total number of 131 differentially expressed immune-related genes (DEIRGs) were identified between low- vs. high-immune subtypes. Out of them GSEA/GSVA results identified and enriched immune- and inflammation-related pathways. In particular, GSVA results indicated that immune-related pathways were activated in high-immune subgroups. The core DEIRG genes that were identified to be involved in CKD development included: the protein tyrosine phosphatase receptor type C (PTPRC; also known as CD45) regulating cell growth and differentiation, an early activation marker (CD69), co-receptor for T cell receptor (CD8A), and T cell co-stimulatory signal (CD28). These core DEIRD genes were further verified by the GSE96804 dataset. We also found a higher proportion of immune cells infiltrating the high-immune subgroup. Furthermore, the four core genes were positively correlated with most immune cell types. ConclusionAmong 131 DEIRG genes, four genes (PTPRC, CD69, CD8A, and CD28) were identified as potential biomarkers associated with the immune cell infiltration in CKD patients, which may provide a novel insight for immunotherapy for CKD.